ANCOVA in Vocabulary taught (Vocabulary taught)

Geiser C. Challco geiser@alumni.usp.br

• Setting Initial Variables
• Descriptive Statistics of Initial Data
• ANCOVA and Pairwise for one factor: grupo
  • Without remove non-normal data
  • Computing ANCOVA and PairWise After removing non-normal data (OK)
    • Plots for ancova
    • Checking linearity assumption
    • Checking normality and homogeneity
• ANCOVA and Pairwise for two factors grupo:Sexo
  • Without remove non-normal data
  • Computing ANCOVA and PairWise After removing non-normal data (OK)
    • Plots for ancova
    • Checking linearity assumption
    • Checking normality and homogeneity
• ANCOVA and Pairwise for two factors grupo:Zona
  • Without remove non-normal data
  • Computing ANCOVA and PairWise After removing non-normal data (OK)
    • Plots for ancova
    • Checking linearity assumption
    • Checking normality and homogeneity
• ANCOVA and Pairwise for two factors grupo:Cor.Raca
  • Without remove non-normal data
  • Computing ANCOVA and PairWise After removing non-normal data (OK)
    • Plots for ancova
    • Checking linearity assumption
    • Checking normality and homogeneity
• ANCOVA and Pairwise for two factors grupo:Serie
  • Without remove non-normal data
  • Computing ANCOVA and PairWise After removing non-normal data (OK)
    • Plots for ancova
    • Checking linearity assumption
    • Checking normality and homogeneity
• ANCOVA and Pairwise for two factors grupo:vocab.teach.quintile
  • Without remove non-normal data
  • Computing ANCOVA and PairWise After removing non-normal data (OK)
    • Plots for ancova
    • Checking linearity assumption
    • Checking normality and homogeneity
• Summary of Results
  • Descriptive Statistics
  • ANCOVA Table Comparison
  • PairWise Table Comparison
  • EMMS Table Comparison
  • CONSORT tables
    • Enrolment participants table
    • Allocated participants table (after removed students with disabilities/special needs)
    • Follow-up participants table (after removed students with does-not have pre and post-test)
    • Participants table for Analysis (after exclude non-normal data)

NOTE:

• Teste ANCOVA para determinar se houve diferenças significativas no Vocabulary taught (medido usando pre- e pos-testes).
• ANCOVA test to determine whether there were significant differences in Vocabulary taught (measured using pre- and post-tests).

Setting Initial Variables

dv = "vocab.teach"
dv.pos = "vocab.teach.pos"
dv.pre = "vocab.teach.pre"

fatores2 <- c("Sexo","Zona","Cor.Raca","Serie","vocab.teach.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2

fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1

lfatores <- c(lfatores1)

color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#008000")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
  "Parda"="#b97100","Indígena"="#9F262F",
  "Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)

level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")

# ..

ymin <- 0
ymax <- 0

ymin.ci <- 0
ymax.ci <- 0


color[["grupo:Sexo"]] = c(
  "Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
  "Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
  "Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
  "Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
  "Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
  "Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
  "Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
  "Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
    "Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
    "Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
    "Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
    "Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
    "Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
    
    "Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
    "Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
    "Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
    "Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
    "Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)


for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
               "TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
               "leitura.compreensao")) {
  color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
  level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
  color[[paste0("grupo:",coln,".quintile")]] = c(
    "Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
    "Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
    "Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
    "Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
    "Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
    
    "Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
    "Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
    "Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
    "Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
    "Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}

tdat <- read_excel("../data/data.xlsx", sheet = "sumary")
tdat <- tdat[!is.na(tdat[["WG.Grupo"]]),]
tdat$grupo <- factor(tdat[["WG.Grupo"]], level[["grupo"]]) 

gdat <- tdat[which(is.na(tdat$Necessidade.Deficiencia) & !is.na(tdat$WG.Grupo)),]

dat <- gdat
dat$grupo <- factor(dat[["WG.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
  dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]

dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])


for (f in c("grupo", names(lfatores))) {
  if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0) 
      color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
  if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
    color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}

ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()

Descriptive Statistics of Initial Data

df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"), 
                       include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
  df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
    if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
      get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
                       symmetry.test = T, normality.test = F)
    }))
)

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
## There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
            colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]

grupo	Sexo	Zona	Cor.Raca	Serie	vocab.teach.quintile	variable	n	mean	median	min	max	sd	se	ci	iqr	symmetry	skewness	kurtosis
Controle						vocab.teach.pre	516	4.506	4.0	0	13	2.164	0.095	0.187	3.00	YES	0.358	-0.156
Experimental						vocab.teach.pre	713	4.759	5.0	0	13	2.018	0.076	0.148	3.00	YES	0.383	-0.021
						vocab.teach.pre	1229	4.653	4.0	0	13	2.083	0.059	0.117	3.00	YES	0.358	-0.073
Controle						vocab.teach.pos	516	4.919	5.0	0	15	2.294	0.101	0.198	3.00	YES	0.295	0.106
Experimental						vocab.teach.pos	713	5.401	5.0	0	12	2.273	0.085	0.167	3.00	YES	0.206	-0.390
						vocab.teach.pos	1229	5.199	5.0	0	15	2.293	0.065	0.128	3.00	YES	0.237	-0.188
Controle	F					vocab.teach.pre	259	4.587	5.0	0	10	2.032	0.126	0.249	3.00	YES	0.139	-0.532
Controle	M					vocab.teach.pre	257	4.424	4.0	0	13	2.290	0.143	0.281	3.00	NO	0.531	0.058
Experimental	F					vocab.teach.pre	368	4.777	5.0	0	13	2.063	0.108	0.211	3.00	YES	0.375	0.137
Experimental	M					vocab.teach.pre	345	4.739	5.0	1	11	1.971	0.106	0.209	3.00	YES	0.387	-0.257
Controle	F					vocab.teach.pos	259	5.058	5.0	0	15	2.303	0.143	0.282	2.00	YES	0.337	0.675
Controle	M					vocab.teach.pos	257	4.778	5.0	0	11	2.281	0.142	0.280	3.00	YES	0.249	-0.536
Experimental	F					vocab.teach.pos	368	5.698	6.0	0	12	2.249	0.117	0.230	3.00	YES	0.212	-0.394
Experimental	M					vocab.teach.pos	345	5.084	5.0	0	12	2.258	0.122	0.239	4.00	YES	0.214	-0.426
Controle		Rural				vocab.teach.pre	253	4.462	4.0	0	11	2.069	0.130	0.256	3.00	YES	0.324	-0.388
Controle		Urbana				vocab.teach.pre	114	4.412	4.0	0	10	1.968	0.184	0.365	2.75	YES	0.306	-0.024
Controle						vocab.teach.pre	149	4.651	5.0	0	13	2.452	0.201	0.397	3.00	YES	0.343	-0.289
Experimental		Rural				vocab.teach.pre	294	4.694	5.0	0	11	2.068	0.121	0.237	3.00	YES	0.289	-0.366
Experimental		Urbana				vocab.teach.pre	189	4.815	5.0	1	13	2.127	0.155	0.305	3.00	NO	0.538	0.429
Experimental						vocab.teach.pre	230	4.796	4.5	1	10	1.861	0.123	0.242	2.00	YES	0.354	-0.235
Controle		Rural				vocab.teach.pos	253	5.016	5.0	0	15	2.384	0.150	0.295	4.00	YES	0.372	0.263
Controle		Urbana				vocab.teach.pos	114	4.833	5.0	0	10	2.052	0.192	0.381	2.00	YES	0.125	-0.173
Controle						vocab.teach.pos	149	4.819	5.0	0	11	2.322	0.190	0.376	3.00	YES	0.206	-0.328
Experimental		Rural				vocab.teach.pos	294	5.374	5.0	0	12	2.358	0.138	0.271	3.00	YES	0.168	-0.455
Experimental		Urbana				vocab.teach.pos	189	5.487	6.0	1	11	2.163	0.157	0.310	3.00	YES	0.225	-0.404
Experimental						vocab.teach.pos	230	5.365	5.0	1	12	2.258	0.149	0.293	3.00	YES	0.256	-0.363
Controle			Parda			vocab.teach.pre	168	4.458	4.0	0	13	2.166	0.167	0.330	3.00	NO	0.546	0.619
Controle			Indígena			vocab.teach.pre	13	4.615	4.0	1	7	1.660	0.460	1.003	2.00	YES	-0.338	-0.464
Controle			Branca			vocab.teach.pre	54	4.352	4.0	1	11	2.267	0.308	0.619	3.75	NO	0.590	-0.056
Controle			Preta			vocab.teach.pre	1	3.000	3.0	3	3				0.00	few data	0.000	0.000
Controle						vocab.teach.pre	280	4.564	4.0	0	10	2.173	0.130	0.256	3.00	YES	0.204	-0.672
Experimental			Parda			vocab.teach.pre	201	4.478	4.0	0	10	2.057	0.145	0.286	3.00	YES	0.454	-0.356
Experimental			Indígena			vocab.teach.pre	18	4.167	4.0	1	7	1.654	0.390	0.822	1.75	YES	-0.250	-1.004
Experimental			Branca			vocab.teach.pre	62	5.000	5.0	1	8	1.765	0.224	0.448	2.00	YES	-0.123	-0.789
Experimental			Preta			vocab.teach.pre	1	1.000	1.0	1	1				0.00	few data	0.000	0.000
Experimental			Amarela			vocab.teach.pre	1	3.000	3.0	3	3				0.00	few data	0.000	0.000
Experimental						vocab.teach.pre	430	4.893	5.0	1	13	2.028	0.098	0.192	2.75	YES	0.424	0.186
Controle			Parda			vocab.teach.pos	168	5.006	5.0	0	10	2.265	0.175	0.345	2.50	YES	0.079	-0.377
Controle			Indígena			vocab.teach.pos	13	5.923	6.0	2	9	2.100	0.582	1.269	3.00	YES	-0.305	-1.134
Controle			Branca			vocab.teach.pos	54	4.889	5.0	1	11	2.408	0.328	0.657	4.00	YES	0.295	-0.654
Controle			Preta			vocab.teach.pos	1	6.000	6.0	6	6				0.00	few data	0.000	0.000
Controle						vocab.teach.pos	280	4.821	5.0	0	15	2.299	0.137	0.270	3.00	YES	0.458	0.620
Experimental			Parda			vocab.teach.pos	201	5.318	5.0	0	12	2.364	0.167	0.329	3.00	YES	0.381	-0.144
Experimental			Indígena			vocab.teach.pos	18	5.111	5.0	2	9	2.026	0.478	1.007	3.50	YES	0.098	-1.067
Experimental			Branca			vocab.teach.pos	62	5.387	6.0	1	10	2.335	0.297	0.593	4.00	YES	-0.030	-1.012
Experimental			Preta			vocab.teach.pos	1	4.000	4.0	4	4				0.00	few data	0.000	0.000
Experimental			Amarela			vocab.teach.pos	1	6.000	6.0	6	6				0.00	few data	0.000	0.000
Experimental						vocab.teach.pos	430	5.456	5.0	0	11	2.239	0.108	0.212	3.00	YES	0.150	-0.452
Controle				6 ano		vocab.teach.pre	146	3.925	4.0	0	10	1.901	0.157	0.311	3.00	YES	0.418	-0.039
Controle				7 ano		vocab.teach.pre	152	4.013	4.0	1	11	1.919	0.156	0.308	2.00	NO	0.685	0.547
Controle				8 ano		vocab.teach.pre	100	4.560	4.5	0	10	2.298	0.230	0.456	3.00	YES	0.050	-0.769
Controle				9 ano		vocab.teach.pre	118	5.814	6.0	1	13	2.108	0.194	0.384	3.00	YES	0.043	0.248
Experimental				6 ano		vocab.teach.pre	165	4.255	4.0	1	11	1.902	0.148	0.292	2.00	NO	0.618	0.553
Experimental				7 ano		vocab.teach.pre	196	4.745	5.0	1	13	2.157	0.154	0.304	3.00	NO	0.549	0.323
Experimental				8 ano		vocab.teach.pre	181	4.796	5.0	1	9	1.954	0.145	0.287	3.00	YES	0.175	-0.643
Experimental				9 ano		vocab.teach.pre	171	5.222	5.0	0	11	1.927	0.147	0.291	3.00	YES	0.171	-0.238
Controle				6 ano		vocab.teach.pos	146	4.158	4.0	0	9	1.957	0.162	0.320	3.00	YES	0.170	-0.451
Controle				7 ano		vocab.teach.pos	152	4.658	5.0	0	10	2.040	0.165	0.327	3.00	YES	0.287	-0.267
Controle				8 ano		vocab.teach.pos	100	5.070	5.0	0	10	2.508	0.251	0.498	4.00	YES	-0.166	-0.786
Controle				9 ano		vocab.teach.pos	118	6.068	6.0	1	15	2.360	0.217	0.430	2.75	YES	0.427	0.657
Experimental				6 ano		vocab.teach.pos	165	5.164	5.0	0	11	2.346	0.183	0.361	4.00	YES	0.244	-0.620
Experimental				7 ano		vocab.teach.pos	196	4.980	5.0	0	11	2.134	0.152	0.301	3.00	YES	0.405	-0.068
Experimental				8 ano		vocab.teach.pos	181	5.210	5.0	1	11	2.188	0.163	0.321	3.00	YES	0.356	-0.343
Experimental				9 ano		vocab.teach.pos	171	6.316	6.0	0	12	2.211	0.169	0.334	3.00	YES	-0.202	0.117
Controle					1st quintile	vocab.teach.pre	102	1.618	2.0	0	2	0.598	0.059	0.117	1.00	few data	0.000	0.000
Controle					2nd quintile	vocab.teach.pre	80	3.000	3.0	3	3	0.000	0.000	0.000	0.00	few data	0.000	0.000
Controle					3rd quintile	vocab.teach.pre	173	4.509	5.0	4	5	0.501	0.038	0.075	1.00	few data	0.000	0.000
Controle					4th quintile	vocab.teach.pre	64	6.000	6.0	6	6	0.000	0.000	0.000	0.00	few data	0.000	0.000
Controle					5th quintile	vocab.teach.pre	97	7.794	7.0	7	13	1.060	0.108	0.214	1.00	NO	1.866	5.018
Experimental					1st quintile	vocab.teach.pre	93	1.710	2.0	0	2	0.480	0.050	0.099	1.00	few data	0.000	0.000
Experimental					2nd quintile	vocab.teach.pre	105	3.000	3.0	3	3	0.000	0.000	0.000	0.00	few data	0.000	0.000
Experimental					3rd quintile	vocab.teach.pre	273	4.451	4.0	4	5	0.498	0.030	0.059	1.00	few data	0.000	0.000
Experimental					4th quintile	vocab.teach.pre	99	6.000	6.0	6	6	0.000	0.000	0.000	0.00	few data	0.000	0.000
Experimental					5th quintile	vocab.teach.pre	143	7.762	7.0	7	13	1.034	0.086	0.171	1.00	NO	1.809	4.529
Controle					1st quintile	vocab.teach.pos	102	3.431	3.0	0	10	1.927	0.191	0.378	2.75	YES	0.412	0.170
Controle					2nd quintile	vocab.teach.pos	80	4.138	4.0	0	9	1.674	0.187	0.373	2.00	YES	0.265	0.205
Controle					3rd quintile	vocab.teach.pos	173	4.694	5.0	0	15	2.047	0.156	0.307	3.00	NO	0.746	2.761
Controle					4th quintile	vocab.teach.pos	64	5.922	6.0	0	10	2.125	0.266	0.531	2.25	NO	-0.573	-0.201
Controle					5th quintile	vocab.teach.pos	97	6.866	7.0	2	11	2.024	0.206	0.408	2.00	YES	-0.245	-0.452
Experimental					1st quintile	vocab.teach.pos	93	3.871	3.0	1	10	1.946	0.202	0.401	2.00	NO	0.617	-0.028
Experimental					2nd quintile	vocab.teach.pos	105	4.686	5.0	1	9	1.723	0.168	0.333	3.00	YES	0.107	-0.648
Experimental					3rd quintile	vocab.teach.pos	273	5.154	5.0	0	12	2.058	0.125	0.245	3.00	YES	0.228	-0.010
Experimental					4th quintile	vocab.teach.pos	99	5.778	6.0	0	11	2.183	0.219	0.435	3.00	YES	-0.155	-0.146
Experimental					5th quintile	vocab.teach.pos	143	7.133	7.0	1	12	2.173	0.182	0.359	3.00	YES	-0.276	-0.418

ANCOVA and Pairwise for one factor: grupo

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "vocab.teach.pos", "grupo")

pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])

aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)

pwc <- emmeans_test(pdat, vocab.teach.pos ~ grupo, covariate = vocab.teach.pre,
                    p.adjust.method = "bonferroni")

pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
                          vocab.teach ~ time,
                          p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)

ds <- get.descriptives(pdat, "vocab.teach.pos", "grupo", covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
            ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
            by = "grupo", all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
            "emmean","se.emms","conf.low","conf.high")]

colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
                  paste0(c("M","SE"), " (unadj)"),
                  paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")

lemms[["grupo"]] <- ds

Computing ANCOVA and PairWise After removing non-normal data (OK)

wdat = pdat 

res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)

wdat = wdat[!wdat$id %in% non.normal,]

wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])

ldat[["grupo"]] = wdat

(non.normal)

## [1] "P3709"

aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
                            by="Effect", suffixes = c("","'"))

(df = get_anova_table(aov))

## ANOVA Table (type II tests)
## 
##            Effect DFn  DFd       F        p p<.05   ges
## 1 vocab.teach.pre   1 1225 380.801 4.64e-74     * 0.237
## 2           grupo   1 1225  10.282 1.00e-03     * 0.008

Effect	DFn	DFd	F	p	p<.05	ges
vocab.teach.pre	1	1225	380.801	0.000	*	0.237
grupo	1	1225	10.282	0.001	*	0.008

pwc <- emmeans_test(wdat, vocab.teach.pos ~ grupo, covariate = vocab.teach.pre,
                    p.adjust.method = "bonferroni")

term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1225	-3.207	0.001	0.001	**

pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
                         vocab.teach ~ time,
                         p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
                            by=c("grupo","term",".y.","group1","group2"),
                            suffixes = c("","'"))

grupo	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
Controle	time	vocab.teach	pre	pos	2452	-2.909	0.004	0.004	**
Experimental	time	vocab.teach	pre	pos	2452	-5.577	0.000	0.000	****

ds <- get.descriptives(wdat, "vocab.teach.pos", "grupo", covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
            ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
            by = "grupo", all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
            "emmean","se.emms","conf.low","conf.high")]

colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
                  paste0(c("M","SE"), " (unadj)"),
                  paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")

lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))

grupo	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high
Controle	515	4.505	0.095	4.899	0.099	4.977	0.087	4.806	5.148
Experimental	713	4.759	0.076	5.401	0.085	5.345	0.074	5.199	5.490

Plots for ancova

plots <- oneWayAncovaPlots(
  wdat, "vocab.teach.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
  font.label.size=10, step.increase=0.05, p.label="p.adj",
  subtitle = which(aov$Effect == "grupo"))

if (!is.null(nrow(plots[["grupo"]]$data)))
  plots[["grupo"]] +
  ggplot2::ylab("Vocabulary (post-test)") +
  theme(axis.title = element_text(size = 14),
        legend.text = element_text(size = 16),
        plot.subtitle = element_text(size = 18)) +
  if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
  wdat, "vocab.teach.pos", "grupo", aov, pwc, covar = "vocab.teach.pre",
  theme = "classic", color = color[["grupo"]],
  subtitle = which(aov$Effect == "grupo"))

if (length(unique(wdat[["grupo"]])) > 1)
  plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
  plots <- oneWayAncovaBoxPlots(
    wdat.long, "vocab.teach", "grupo", aov, pwc.long,
    pre.post = "time", theme = "classic", color = color$prepost)

if (length(unique(wdat.long[["grupo"]])) > 1)
  plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax) 

Checking linearity assumption

ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.75,
          color = "grupo", add = "reg.line")+
  stat_regline_equation(
    aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
  ) +
  ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
  ggplot2::scale_color_manual(values = color[["grupo"]]) +
  ggplot2::ylab("Vocabulary (post-test)") +
  ggplot2::xlab("Vocabulary (pre-test)") +
  theme(axis.title = element_text(size = 14),
        legend.text = element_text(size = 16),
        plot.subtitle = element_text(size = 18)) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)

## Warning: The dot-dot notation (`..eq.label..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(eq.label)` instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
##   Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Checking normality and homogeneity

res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo, data = wdat))

shapiro_test(res$.resid)

## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.998   0.263

levene_test(res, .resid ~ grupo)

## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     1  1226      2.40 0.121

ANCOVA and Pairwise for two factors grupo:Sexo

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
                         "vocab.teach.pos", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
  pdat[["Sexo"]],
  level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])

if (length(unique(pdat[["Sexo"]])) >= 2) {
  aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo)
  laov[["grupo:Sexo"]] <- get_anova_table(aov)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwcs <- list()
  pwcs[["Sexo"]] <- emmeans_test(
    group_by(pdat, grupo), vocab.teach.pos ~ Sexo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Sexo), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
  pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Sexo"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Sexo"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Sexo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Sexo"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Sexo"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
  
  
  ldat[["grupo:Sexo"]] = wdat
  
  (non.normal)
}

## [1] "P3709"

if (length(unique(pdat[["Sexo"]])) >= 2) {
  aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo)
  laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}

Effect	DFn	DFd	F	p	p<.05	ges
vocab.teach.pre	1	1223	383.236	0.000	*	0.239
grupo	1	1223	10.087	0.002	*	0.008
Sexo	1	1223	13.356	0.000	*	0.011
grupo:Sexo	1	1223	3.710	0.054		0.003

if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwcs <- list()
  pwcs[["Sexo"]] <- emmeans_test(
    group_by(wdat, grupo), vocab.teach.pos ~ Sexo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Sexo), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
  pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}

grupo	Sexo	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
	F	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1223	-3.615	0.000	0.000	***
	M	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1223	-0.859	0.391	0.391	ns
Controle		vocab.teach.pre*Sexo	vocab.teach.pos	F	M	1223	0.900	0.368	0.368	ns
Experimental		vocab.teach.pre*Sexo	vocab.teach.pos	F	M	1223	4.032	0.000	0.000	****

if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Sexo"]],
                                         by=c("grupo","Sexo","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}

grupo	Sexo	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
Controle	F	time	vocab.teach	pre	pos	2448	-2.273	0.023	0.023	*
Controle	M	time	vocab.teach	pre	pos	2448	-1.851	0.064	0.064	ns
Experimental	F	time	vocab.teach	pre	pos	2448	-5.761	0.000	0.000	****
Experimental	M	time	vocab.teach	pre	pos	2448	-2.089	0.037	0.037	*

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Sexo"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Sexo"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Sexo","n","mean.vocab.teach.pre","se.vocab.teach.pre",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
                                          by=c("grupo","Sexo"), suffixes = c("","'"))
}

grupo	Sexo	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high
Controle	F	258	4.585	0.127	5.019	0.138	5.055	0.122	4.815	5.295
Controle	M	257	4.424	0.143	4.778	0.142	4.899	0.123	4.658	5.140
Experimental	F	368	4.777	0.108	5.698	0.117	5.632	0.103	5.431	5.834
Experimental	M	345	4.739	0.106	5.084	0.122	5.038	0.106	4.830	5.246

Plots for ancova

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "Vocabulary taught",
             subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Sexo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "Vocabulary taught",
               subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      ggplot2::ylab("Vocabulary (post-test)") +
      theme(axis.title = element_text(size = 14),
            legend.text = element_text(size = 16),
            plot.subtitle = element_text(size = 18)) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "vocab.teach.pos", c("grupo","Sexo"), aov, pwcs, covar = "vocab.teach.pre",
    theme = "classic", color = color[["grupo:Sexo"]],
    subtitle = which(aov$Effect == "grupo:Sexo"))
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary taught") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "vocab.teach", c("grupo","Sexo"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}

if (length(unique(pdat[["Sexo"]])) >= 2) 
  plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary taught") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            facet.by = c("grupo","Sexo"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    )  +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "grupo", facet.by = "Sexo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "Sexo", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
    ggplot2::scale_color_manual(values = color[["Sexo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Sexo"]])) >= 2) 
  res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo, data = wdat))

if (length(unique(pdat[["Sexo"]])) >= 2)
  shapiro_test(res$.resid)

## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.999   0.459

if (length(unique(pdat[["Sexo"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Sexo)

## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     3  1224     0.660 0.577

ANCOVA and Pairwise for two factors grupo:Zona

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
                         "vocab.teach.pos", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
  pdat[["Zona"]],
  level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])

if (length(unique(pdat[["Zona"]])) >= 2) {
  aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Zona)
  laov[["grupo:Zona"]] <- get_anova_table(aov)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  pwcs <- list()
  pwcs[["Zona"]] <- emmeans_test(
    group_by(pdat, grupo), vocab.teach.pos ~ Zona,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Zona), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
  pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Zona"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Zona"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Zona","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Zona"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Zona"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Zona, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
  
  
  ldat[["grupo:Zona"]] = wdat
  
  (non.normal)
}

## [1] "P3709"

if (length(unique(pdat[["Zona"]])) >= 2) {
  aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Zona)
  laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}

Effect	DFn	DFd	F	p	p<.05	ges
vocab.teach.pre	1	844	287.941	0.000	*	0.254
grupo	1	844	5.704	0.017	*	0.007
Zona	1	844	0.020	0.887		0.000
grupo:Zona	1	844	0.323	0.570		0.000

if (length(unique(pdat[["Zona"]])) >= 2) {
  pwcs <- list()
  pwcs[["Zona"]] <- emmeans_test(
    group_by(wdat, grupo), vocab.teach.pos ~ Zona,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Zona), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
  pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}

grupo	Zona	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
	Rural	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	844	-1.605	0.109	0.109	ns
	Urbana	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	844	-1.862	0.063	0.063	ns
Controle		vocab.teach.pre*Zona	vocab.teach.pos	Rural	Urbana	844	0.528	0.598	0.598	ns
Experimental		vocab.teach.pre*Zona	vocab.teach.pos	Rural	Urbana	844	-0.253	0.801	0.801	ns

if (length(unique(pdat[["Zona"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Zona"]],
                                         by=c("grupo","Zona","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}

grupo	Zona	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
Controle	Rural	time	vocab.teach	pre	pos	1690	-2.672	0.008	0.008	**
Controle	Urbana	time	vocab.teach	pre	pos	1690	-1.467	0.143	0.143	ns
Experimental	Rural	time	vocab.teach	pre	pos	1690	-3.806	0.000	0.000	***
Experimental	Urbana	time	vocab.teach	pre	pos	1690	-3.015	0.003	0.003	**

if (length(unique(pdat[["Zona"]])) >= 2) {
  ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Zona"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Zona"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Zona","n","mean.vocab.teach.pre","se.vocab.teach.pre",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
                                          by=c("grupo","Zona"), suffixes = c("","'"))
}

grupo	Zona	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high
Controle	Rural	252	4.460	0.131	4.976	0.145	5.061	0.123	4.819	5.302
Controle	Urbana	114	4.412	0.184	4.833	0.192	4.944	0.183	4.585	5.303
Experimental	Rural	294	4.694	0.121	5.374	0.138	5.330	0.114	5.106	5.554
Experimental	Urbana	189	4.815	0.155	5.487	0.157	5.376	0.142	5.097	5.655

Plots for ancova

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "Vocabulary taught",
             subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Zona"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "Vocabulary taught",
               subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      ggplot2::ylab("Vocabulary (post-test)") +
      theme(axis.title = element_text(size = 14),
            legend.text = element_text(size = 16),
            plot.subtitle = element_text(size = 18)) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "vocab.teach.pos", c("grupo","Zona"), aov, pwcs, covar = "vocab.teach.pre",
    theme = "classic", color = color[["grupo:Zona"]],
    subtitle = which(aov$Effect == "grupo:Zona"))
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary taught") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "vocab.teach", c("grupo","Zona"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}

if (length(unique(pdat[["Zona"]])) >= 2) 
  plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary taught") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            facet.by = c("grupo","Zona"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "grupo", facet.by = "Zona", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "Zona", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
    ggplot2::scale_color_manual(values = color[["Zona"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Zona"]])) >= 2) 
  res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Zona, data = wdat))

if (length(unique(pdat[["Zona"]])) >= 2)
  shapiro_test(res$.resid)

## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.998   0.498

if (length(unique(pdat[["Zona"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Zona)

## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     3   845      1.37 0.252

ANCOVA and Pairwise for two factors grupo:Cor.Raca

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
                         "vocab.teach.pos", c("grupo","Cor.Raca"))

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
  pdat[["Cor.Raca"]],
  level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca)
  laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwcs <- list()
  pwcs[["Cor.Raca"]] <- emmeans_test(
    group_by(pdat, grupo), vocab.teach.pos ~ Cor.Raca,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Cor.Raca), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
  pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Cor.Raca"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Cor.Raca"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
  
  
  ldat[["grupo:Cor.Raca"]] = wdat
  
  (non.normal)
}

## NULL

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca)
  laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}

Effect	DFn	DFd	F	p	p<.05	ges
vocab.teach.pre	1	509	188.846	0.000	*	0.271
grupo	1	509	1.436	0.231		0.003
Cor.Raca	2	509	0.773	0.462		0.003
grupo:Cor.Raca	2	509	0.683	0.506		0.003

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwcs <- list()
  pwcs[["Cor.Raca"]] <- emmeans_test(
    group_by(wdat, grupo), vocab.teach.pos ~ Cor.Raca,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Cor.Raca), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
  pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}

grupo	Cor.Raca	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
	Parda	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	509	-1.455	0.146	0.146	ns
	Indígena	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	509	0.763	0.446	0.446	ns
	Branca	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	509	-0.324	0.746	0.746	ns
Controle		vocab.teach.pre*Cor.Raca	vocab.teach.pos	Parda	Indígena	509	-1.448	0.148	0.445	ns
Controle		vocab.teach.pre*Cor.Raca	vocab.teach.pos	Parda	Branca	509	0.177	0.860	1.000	ns
Controle		vocab.teach.pre*Cor.Raca	vocab.teach.pos	Indígena	Branca	509	1.438	0.151	0.453	ns
Experimental		vocab.teach.pre*Cor.Raca	vocab.teach.pos	Parda	Indígena	509	0.053	0.958	1.000	ns
Experimental		vocab.teach.pre*Cor.Raca	vocab.teach.pos	Parda	Branca	509	0.819	0.413	1.000	ns
Experimental		vocab.teach.pre*Cor.Raca	vocab.teach.pos	Indígena	Branca	509	0.396	0.692	1.000	ns

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Cor.Raca"]],
                                         by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}

grupo	Cor.Raca	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
Controle	Parda	time	vocab.teach	pre	pos	1020	-2.288	0.022	0.022	*
Controle	Indígena	time	vocab.teach	pre	pos	1020	-1.520	0.129	0.129	ns
Controle	Branca	time	vocab.teach	pre	pos	1020	-1.272	0.204	0.204	ns
Experimental	Parda	time	vocab.teach	pre	pos	1020	-3.842	0.000	0.000	***
Experimental	Indígena	time	vocab.teach	pre	pos	1020	-1.292	0.197	0.197	ns
Experimental	Branca	time	vocab.teach	pre	pos	1020	-0.982	0.326	0.326	ns

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Cor.Raca"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.teach.pre","se.vocab.teach.pre",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
                                          by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}

grupo	Cor.Raca	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high
Controle	Branca	54	4.352	0.308	4.889	0.328	4.983	0.270	4.454	5.513
Controle	Indígena	13	4.615	0.460	5.923	0.582	5.864	0.549	4.784	6.943
Controle	Parda	168	4.458	0.167	5.006	0.175	5.038	0.153	4.738	5.338
Experimental	Branca	62	5.000	0.224	5.387	0.297	5.103	0.252	4.607	5.599
Experimental	Indígena	18	4.167	0.390	5.111	0.478	5.314	0.467	4.396	6.231
Experimental	Parda	201	4.478	0.145	5.318	0.167	5.339	0.140	5.065	5.614

Plots for ancova

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "Vocabulary taught",
             subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "Vocabulary taught",
               subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      ggplot2::ylab("Vocabulary (post-test)") +
      theme(axis.title = element_text(size = 14),
            legend.text = element_text(size = 16),
            plot.subtitle = element_text(size = 18)) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "vocab.teach.pos", c("grupo","Cor.Raca"), aov, pwcs, covar = "vocab.teach.pre",
    theme = "classic", color = color[["grupo:Cor.Raca"]],
    subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary taught") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "vocab.teach", c("grupo","Cor.Raca"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) 
  plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary taught") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
    ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Cor.Raca"]])) >= 2) 
  res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca, data = wdat))

if (length(unique(pdat[["Cor.Raca"]])) >= 2)
  shapiro_test(res$.resid)

## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.998   0.919

if (length(unique(pdat[["Cor.Raca"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Cor.Raca)

## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     5   510     0.703 0.621

ANCOVA and Pairwise for two factors grupo:Serie

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
                         "vocab.teach.pos", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
  pdat[["Serie"]],
  level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])

if (length(unique(pdat[["Serie"]])) >= 2) {
  aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Serie)
  laov[["grupo:Serie"]] <- get_anova_table(aov)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  pwcs <- list()
  pwcs[["Serie"]] <- emmeans_test(
    group_by(pdat, grupo), vocab.teach.pos ~ Serie,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Serie), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
  pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Serie"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Serie"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Serie","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Serie"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Serie"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Serie, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
  
  
  ldat[["grupo:Serie"]] = wdat
  
  (non.normal)
}

## [1] "P3709"

if (length(unique(pdat[["Serie"]])) >= 2) {
  aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Serie)
  laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}

Effect	DFn	DFd	F	p	p<.05	ges
vocab.teach.pre	1	1219	325.734	0.000	*	0.211
grupo	1	1219	10.035	0.002	*	0.008
Serie	3	1219	11.129	0.000	*	0.027
grupo:Serie	3	1219	3.860	0.009	*	0.009

if (length(unique(pdat[["Serie"]])) >= 2) {
  pwcs <- list()
  pwcs[["Serie"]] <- emmeans_test(
    group_by(wdat, grupo), vocab.teach.pos ~ Serie,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Serie), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
  pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}

grupo	Serie	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
	6 ano	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1219	-3.797	0.000	0.000	***
	7 ano	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1219	0.209	0.834	0.834	ns
	8 ano	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1219	-0.091	0.927	0.927	ns
	9 ano	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1219	-2.661	0.008	0.008	**
Controle		vocab.teach.pre*Serie	vocab.teach.pos	6 ano	7 ano	1219	-2.021	0.044	0.261	ns
Controle		vocab.teach.pre*Serie	vocab.teach.pos	6 ano	8 ano	1219	-2.347	0.019	0.115	ns
Controle		vocab.teach.pre*Serie	vocab.teach.pos	6 ano	9 ano	1219	-3.582	0.000	0.002	**
Controle		vocab.teach.pre*Serie	vocab.teach.pos	7 ano	8 ano	1219	-0.552	0.581	1.000	ns
Controle		vocab.teach.pre*Serie	vocab.teach.pos	7 ano	9 ano	1219	-1.756	0.079	0.476	ns
Controle		vocab.teach.pre*Serie	vocab.teach.pos	8 ano	9 ano	1219	-1.088	0.277	1.000	ns
Experimental		vocab.teach.pre*Serie	vocab.teach.pos	6 ano	7 ano	1219	2.081	0.038	0.226	ns
Experimental		vocab.teach.pre*Serie	vocab.teach.pos	6 ano	8 ano	1219	1.067	0.286	1.000	ns
Experimental		vocab.teach.pre*Serie	vocab.teach.pos	6 ano	9 ano	1219	-3.119	0.002	0.011	*
Experimental		vocab.teach.pre*Serie	vocab.teach.pos	7 ano	8 ano	1219	-1.021	0.307	1.000	ns
Experimental		vocab.teach.pre*Serie	vocab.teach.pos	7 ano	9 ano	1219	-5.373	0.000	0.000	****
Experimental		vocab.teach.pre*Serie	vocab.teach.pos	8 ano	9 ano	1219	-4.290	0.000	0.000	***

if (length(unique(pdat[["Serie"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Serie"]],
                                         by=c("grupo","Serie","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}

grupo	Serie	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
Controle	6 ano	time	vocab.teach	pre	pos	2440	-0.946	0.344	0.344	ns
Controle	7 ano	time	vocab.teach	pre	pos	2440	-2.671	0.008	0.008	**
Controle	8 ano	time	vocab.teach	pre	pos	2440	-1.714	0.087	0.087	ns
Controle	9 ano	time	vocab.teach	pre	pos	2440	-0.621	0.534	0.534	ns
Experimental	6 ano	time	vocab.teach	pre	pos	2440	-3.924	0.000	0.000	****
Experimental	7 ano	time	vocab.teach	pre	pos	2440	-1.104	0.270	0.270	ns
Experimental	8 ano	time	vocab.teach	pre	pos	2440	-1.873	0.061	0.061	ns
Experimental	9 ano	time	vocab.teach	pre	pos	2440	-4.806	0.000	0.000	****

if (length(unique(pdat[["Serie"]])) >= 2) {
  ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Serie"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Serie"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Serie","n","mean.vocab.teach.pre","se.vocab.teach.pre",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
                                          by=c("grupo","Serie"), suffixes = c("","'"))
}

grupo	Serie	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high
Controle	6 ano	146	3.925	0.157	4.158	0.162	4.521	0.162	4.203	4.840
Controle	7 ano	152	4.013	0.156	4.658	0.165	4.978	0.159	4.666	5.289
Controle	8 ano	100	4.560	0.230	5.070	0.251	5.116	0.195	4.734	5.498
Controle	9 ano	117	5.821	0.196	5.991	0.205	5.407	0.183	5.048	5.766
Experimental	6 ano	165	4.255	0.148	5.164	0.183	5.363	0.152	5.064	5.661
Experimental	7 ano	196	4.745	0.154	4.980	0.152	4.933	0.139	4.660	5.206
Experimental	8 ano	181	4.796	0.145	5.210	0.163	5.138	0.145	4.854	5.422
Experimental	9 ano	171	5.222	0.147	6.316	0.169	6.031	0.150	5.737	6.325

Plots for ancova

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "Vocabulary taught",
             subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Serie"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
        plot.subtitle = element_text(size = 18)) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "Vocabulary taught",
               subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      ggplot2::ylab("Vocabulary (post-test)") +
      theme(axis.title = element_text(size = 14),
            legend.text = element_text(size = 16),
            plot.subtitle = element_text(size = 18)) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "vocab.teach.pos", c("grupo","Serie"), aov, pwcs, covar = "vocab.teach.pre",
    theme = "classic", color = color[["grupo:Serie"]],
    subtitle = which(aov$Effect == "grupo:Serie"))
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary taught") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "vocab.teach", c("grupo","Serie"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}

if (length(unique(pdat[["Serie"]])) >= 2) 
  plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary taught") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            facet.by = c("grupo","Serie"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "grupo", facet.by = "Serie", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "Serie", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
    ggplot2::scale_color_manual(values = color[["Serie"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Serie"]])) >= 2) 
  res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Serie, data = wdat))

if (length(unique(pdat[["Serie"]])) >= 2)
  shapiro_test(res$.resid)

## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.998   0.364

if (length(unique(pdat[["Serie"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Serie)

## # A tibble: 1 × 4
##     df1   df2 statistic       p
##   <int> <int>     <dbl>   <dbl>
## 1     7  1220      2.86 0.00583

ANCOVA and Pairwise for two factors grupo:vocab.teach.quintile

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["vocab.teach.quintile"]]),],
                         "vocab.teach.pos", c("grupo","vocab.teach.quintile"))
pdat = pdat[pdat[["vocab.teach.quintile"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["vocab.teach.quintile"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["vocab.teach.quintile"]] = factor(
  pdat[["vocab.teach.quintile"]],
  level[["vocab.teach.quintile"]][level[["vocab.teach.quintile"]] %in% unique(pdat[["vocab.teach.quintile"]])])

pdat.long <- rbind(pdat[,c("id","grupo","vocab.teach.quintile")], pdat[,c("id","grupo","vocab.teach.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile)
  laov[["grupo:vocab.teach.quintile"]] <- get_anova_table(aov)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  pwcs <- list()
  pwcs[["vocab.teach.quintile"]] <- emmeans_test(
    group_by(pdat, grupo), vocab.teach.pos ~ vocab.teach.quintile,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, vocab.teach.quintile), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.teach.quintile"]])
  pwc <- pwc[,c("grupo","vocab.teach.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.teach.quintile")])]
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","vocab.teach.quintile")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:vocab.teach.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","vocab.teach.quintile"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","vocab.teach.quintile"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","vocab.teach.quintile","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","vocab.teach.quintile", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:vocab.teach.quintile"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","vocab.teach.quintile")], wdat[,c("id","grupo","vocab.teach.quintile")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
  
  
  ldat[["grupo:vocab.teach.quintile"]] = wdat
  
  (non.normal)
}

## [1] "P3709"

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile)
  laov[["grupo:vocab.teach.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:vocab.teach.quintile"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}

Effect	DFn	DFd	F	p	p<.05	ges
vocab.teach.pre	1	1217	20.967	0.000	*	0.017
grupo	1	1217	10.914	0.001	*	0.009
vocab.teach.quintile	4	1217	1.750	0.137		0.006
grupo:vocab.teach.quintile	4	1217	0.986	0.414		0.003

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  pwcs <- list()
  pwcs[["vocab.teach.quintile"]] <- emmeans_test(
    group_by(wdat, grupo), vocab.teach.pos ~ vocab.teach.quintile,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, vocab.teach.quintile), vocab.teach.pos ~ grupo,
    covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.teach.quintile"]])
  pwc <- pwc[,c("grupo","vocab.teach.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.teach.quintile")])]
}

grupo	vocab.teach.quintile	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
	1st quintile	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1217	-1.409	0.159	0.159	ns
	2nd quintile	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1217	-1.869	0.062	0.062	ns
	3rd quintile	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1217	-2.828	0.005	0.005	**
	4th quintile	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1217	0.455	0.649	0.649	ns
	5th quintile	vocab.teach.pre*grupo	vocab.teach.pos	Controle	Experimental	1217	-1.080	0.280	0.280	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	2nd quintile	1217	-0.313	0.754	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	3rd quintile	1217	0.166	0.868	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	4th quintile	1217	-1.093	0.275	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	5th quintile	1217	-1.120	0.263	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	2nd quintile	3rd quintile	1217	0.536	0.592	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	2nd quintile	4th quintile	1217	-1.076	0.282	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	2nd quintile	5th quintile	1217	-1.154	0.249	1.000	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	3rd quintile	4th quintile	1217	-1.966	0.050	0.495	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	3rd quintile	5th quintile	1217	-1.973	0.049	0.487	ns
Controle		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	4th quintile	5th quintile	1217	-0.440	0.660	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	2nd quintile	1217	-0.814	0.416	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	3rd quintile	1217	-0.236	0.813	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	4th quintile	1217	-0.059	0.953	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	1st quintile	5th quintile	1217	-0.965	0.335	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	2nd quintile	3rd quintile	1217	0.627	0.531	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	2nd quintile	4th quintile	1217	0.554	0.580	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	2nd quintile	5th quintile	1217	-0.697	0.486	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	3rd quintile	4th quintile	1217	0.197	0.844	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	3rd quintile	5th quintile	1217	-1.407	0.160	1.000	ns
Experimental		vocab.teach.pre*vocab.teach.quintile	vocab.teach.pos	4th quintile	5th quintile	1217	-1.893	0.059	0.585	ns

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","vocab.teach.quintile")),
                           vocab.teach ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:vocab.teach.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:vocab.teach.quintile"]],
                                         by=c("grupo","vocab.teach.quintile","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}

grupo	vocab.teach.quintile	term	.y.	group1	group2	df	statistic	p	p.adj	p.adj.signif
Controle	1st quintile	time	vocab.teach	pre	pos	2436	-8.813	0.000	0.000	****
Controle	2nd quintile	time	vocab.teach	pre	pos	2436	-4.895	0.000	0.000	****
Controle	3rd quintile	time	vocab.teach	pre	pos	2436	-0.807	0.420	0.420	ns
Controle	4th quintile	time	vocab.teach	pre	pos	2436	0.301	0.764	0.764	ns
Controle	5th quintile	time	vocab.teach	pre	pos	2436	4.396	0.000	0.000	****
Experimental	1st quintile	time	vocab.teach	pre	pos	2436	-10.028	0.000	0.000	****
Experimental	2nd quintile	time	vocab.teach	pre	pos	2436	-8.310	0.000	0.000	****
Experimental	3rd quintile	time	vocab.teach	pre	pos	2436	-5.591	0.000	0.000	****
Experimental	4th quintile	time	vocab.teach	pre	pos	2436	1.064	0.288	0.288	ns
Experimental	5th quintile	time	vocab.teach	pre	pos	2436	3.621	0.000	0.000	***

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), covar = "vocab.teach.pre")
  ds <- merge(ds[ds$variable != "vocab.teach.pre",],
              ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
              by = c("grupo","vocab.teach.quintile"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","vocab.teach.quintile"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","vocab.teach.quintile","n","mean.vocab.teach.pre","se.vocab.teach.pre",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","vocab.teach.quintile", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:vocab.teach.quintile"]] <- merge(ds, lemms[["grupo:vocab.teach.quintile"]],
                                          by=c("grupo","vocab.teach.quintile"), suffixes = c("","'"))
}

grupo	vocab.teach.quintile	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high
Controle	1st quintile	102	1.618	0.059	3.431	0.191	4.759	0.350	4.073	5.446
Controle	2nd quintile	80	3.000	0.000	4.138	0.187	4.861	0.272	4.328	5.393
Controle	3rd quintile	172	4.506	0.038	4.634	0.144	4.698	0.151	4.401	4.995
Controle	4th quintile	64	6.000	0.000	5.922	0.266	5.332	0.279	4.786	5.879
Controle	5th quintile	97	7.794	0.108	6.866	0.206	5.491	0.361	4.783	6.200
Experimental	1st quintile	93	1.710	0.050	3.871	0.202	5.159	0.348	4.476	5.841
Experimental	2nd quintile	105	3.000	0.000	4.686	0.168	5.409	0.249	4.920	5.898
Experimental	3rd quintile	273	4.451	0.030	5.154	0.125	5.242	0.121	5.004	5.480
Experimental	4th quintile	99	6.000	0.000	5.778	0.219	5.188	0.237	4.724	5.652
Experimental	5th quintile	143	7.762	0.086	7.133	0.182	5.772	0.340	5.105	6.439

Plots for ancova

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "vocab.teach.quintile", aov, ylab = "Vocabulary taught",
             subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"), addParam = "errorbar") +
  ggplot2::ylab("Vocabulary (post-test)") +
  theme(axis.title = element_text(size = 14),
        legend.text = element_text(size = 16),
        plot.subtitle = element_text(size = 18)) +
    ggplot2::scale_color_manual(values = color[["vocab.teach.quintile"]]) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ggPlotAoC2(pwcs, "vocab.teach.quintile", "grupo", aov, ylab = "Vocabulary taught",
               subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"), addParam = "errorbar") +
      ggplot2::ylab("Vocabulary (post-test)") +
      theme(axis.title = element_text(size = 14),
            legend.text = element_text(size = 16),
            plot.subtitle = element_text(size = 18)) +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), aov, pwcs, covar = "vocab.teach.pre",
    theme = "classic", color = color[["grupo:vocab.teach.quintile"]],
    subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"))
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  plots[["grupo:vocab.teach.quintile"]] + ggplot2::ylab("Vocabulary taught") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "vocab.teach", c("grupo","vocab.teach.quintile"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) 
  plots[["grupo:vocab.teach.quintile"]] + ggplot2::ylab("Vocabulary taught") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            facet.by = c("grupo","vocab.teach.quintile"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "grupo", facet.by = "vocab.teach.quintile", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.teach.quintile"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
  ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
            color = "vocab.teach.quintile", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = vocab.teach.quintile)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.teach.quintile"))) +
    ggplot2::scale_color_manual(values = color[["vocab.teach.quintile"]]) +
    ggplot2::ylab("Vocabulary (post-test)") +
    ggplot2::xlab("Vocabulary (pre-test)") +
    theme(axis.title = element_text(size = 14),
          legend.text = element_text(size = 16),
          plot.subtitle = element_text(size = 18)) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) 
  res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile, data = wdat))

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
  shapiro_test(res$.resid)

## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.998   0.287

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) 
  levene_test(res, .resid ~ grupo*vocab.teach.quintile)

## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     9  1218      1.43 0.171

Summary of Results

Descriptive Statistics

df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"), 
                       include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
  df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
    if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
      get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
                       symmetry.test = T, normality.test = F)
    }))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
             colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]

grupo	Sexo	Zona	Cor.Raca	Serie	vocab.teach.quintile	variable	n	mean	median	min	max	sd	se	ci	iqr	symmetry	skewness	kurtosis
Controle						vocab.teach.pre	515	4.505	4.0	0	13	2.166	0.095	0.187	3.00	YES	0.359	-0.160
Experimental						vocab.teach.pre	713	4.759	5.0	0	13	2.018	0.076	0.148	3.00	YES	0.383	-0.021
						vocab.teach.pre	1228	4.652	4.0	0	13	2.084	0.059	0.117	3.00	YES	0.358	-0.075
Controle						vocab.teach.pos	515	4.899	5.0	0	11	2.253	0.099	0.195	3.00	YES	0.164	-0.426
Experimental						vocab.teach.pos	713	5.401	5.0	0	12	2.273	0.085	0.167	3.00	YES	0.206	-0.390
						vocab.teach.pos	1228	5.191	5.0	0	12	2.277	0.065	0.127	3.00	YES	0.188	-0.382
Controle	F					vocab.teach.pre	258	4.585	5.0	0	10	2.035	0.127	0.250	3.00	YES	0.141	-0.540
Controle	M					vocab.teach.pre	257	4.424	4.0	0	13	2.290	0.143	0.281	3.00	NO	0.531	0.058
Experimental	F					vocab.teach.pre	368	4.777	5.0	0	13	2.063	0.108	0.211	3.00	YES	0.375	0.137
Experimental	M					vocab.teach.pre	345	4.739	5.0	1	11	1.971	0.106	0.209	3.00	YES	0.387	-0.257
Controle	F					vocab.teach.pos	258	5.019	5.0	0	10	2.222	0.138	0.272	2.00	YES	0.082	-0.294
Controle	M					vocab.teach.pos	257	4.778	5.0	0	11	2.281	0.142	0.280	3.00	YES	0.249	-0.536
Experimental	F					vocab.teach.pos	368	5.698	6.0	0	12	2.249	0.117	0.230	3.00	YES	0.212	-0.394
Experimental	M					vocab.teach.pos	345	5.084	5.0	0	12	2.258	0.122	0.239	4.00	YES	0.214	-0.426
Controle		Rural				vocab.teach.pre	252	4.460	4.0	0	11	2.073	0.131	0.257	3.00	YES	0.326	-0.396
Controle		Urbana				vocab.teach.pre	114	4.412	4.0	0	10	1.968	0.184	0.365	2.75	YES	0.306	-0.024
Experimental		Rural				vocab.teach.pre	294	4.694	5.0	0	11	2.068	0.121	0.237	3.00	YES	0.289	-0.366
Experimental		Urbana				vocab.teach.pre	189	4.815	5.0	1	13	2.127	0.155	0.305	3.00	NO	0.538	0.429
Controle		Rural				vocab.teach.pos	252	4.976	5.0	0	10	2.304	0.145	0.286	4.00	YES	0.142	-0.635
Controle		Urbana				vocab.teach.pos	114	4.833	5.0	0	10	2.052	0.192	0.381	2.00	YES	0.125	-0.173
Experimental		Rural				vocab.teach.pos	294	5.374	5.0	0	12	2.358	0.138	0.271	3.00	YES	0.168	-0.455
Experimental		Urbana				vocab.teach.pos	189	5.487	6.0	1	11	2.163	0.157	0.310	3.00	YES	0.225	-0.404
Controle			Parda			vocab.teach.pre	168	4.458	4.0	0	13	2.166	0.167	0.330	3.00	NO	0.546	0.619
Controle			Indígena			vocab.teach.pre	13	4.615	4.0	1	7	1.660	0.460	1.003	2.00	YES	-0.338	-0.464
Controle			Branca			vocab.teach.pre	54	4.352	4.0	1	11	2.267	0.308	0.619	3.75	NO	0.590	-0.056
Experimental			Parda			vocab.teach.pre	201	4.478	4.0	0	10	2.057	0.145	0.286	3.00	YES	0.454	-0.356
Experimental			Indígena			vocab.teach.pre	18	4.167	4.0	1	7	1.654	0.390	0.822	1.75	YES	-0.250	-1.004
Experimental			Branca			vocab.teach.pre	62	5.000	5.0	1	8	1.765	0.224	0.448	2.00	YES	-0.123	-0.789
Controle			Parda			vocab.teach.pos	168	5.006	5.0	0	10	2.265	0.175	0.345	2.50	YES	0.079	-0.377
Controle			Indígena			vocab.teach.pos	13	5.923	6.0	2	9	2.100	0.582	1.269	3.00	YES	-0.305	-1.134
Controle			Branca			vocab.teach.pos	54	4.889	5.0	1	11	2.408	0.328	0.657	4.00	YES	0.295	-0.654
Experimental			Parda			vocab.teach.pos	201	5.318	5.0	0	12	2.364	0.167	0.329	3.00	YES	0.381	-0.144
Experimental			Indígena			vocab.teach.pos	18	5.111	5.0	2	9	2.026	0.478	1.007	3.50	YES	0.098	-1.067
Experimental			Branca			vocab.teach.pos	62	5.387	6.0	1	10	2.335	0.297	0.593	4.00	YES	-0.030	-1.012
Controle				6 ano		vocab.teach.pre	146	3.925	4.0	0	10	1.901	0.157	0.311	3.00	YES	0.418	-0.039
Controle				7 ano		vocab.teach.pre	152	4.013	4.0	1	11	1.919	0.156	0.308	2.00	NO	0.685	0.547
Controle				8 ano		vocab.teach.pre	100	4.560	4.5	0	10	2.298	0.230	0.456	3.00	YES	0.050	-0.769
Controle				9 ano		vocab.teach.pre	117	5.821	6.0	1	13	2.116	0.196	0.387	3.00	YES	0.034	0.227
Experimental				6 ano		vocab.teach.pre	165	4.255	4.0	1	11	1.902	0.148	0.292	2.00	NO	0.618	0.553
Experimental				7 ano		vocab.teach.pre	196	4.745	5.0	1	13	2.157	0.154	0.304	3.00	NO	0.549	0.323
Experimental				8 ano		vocab.teach.pre	181	4.796	5.0	1	9	1.954	0.145	0.287	3.00	YES	0.175	-0.643
Experimental				9 ano		vocab.teach.pre	171	5.222	5.0	0	11	1.927	0.147	0.291	3.00	YES	0.171	-0.238
Controle				6 ano		vocab.teach.pos	146	4.158	4.0	0	9	1.957	0.162	0.320	3.00	YES	0.170	-0.451
Controle				7 ano		vocab.teach.pos	152	4.658	5.0	0	10	2.040	0.165	0.327	3.00	YES	0.287	-0.267
Controle				8 ano		vocab.teach.pos	100	5.070	5.0	0	10	2.508	0.251	0.498	4.00	YES	-0.166	-0.786
Controle				9 ano		vocab.teach.pos	117	5.991	6.0	1	11	2.219	0.205	0.406	2.00	YES	0.062	-0.525
Experimental				6 ano		vocab.teach.pos	165	5.164	5.0	0	11	2.346	0.183	0.361	4.00	YES	0.244	-0.620
Experimental				7 ano		vocab.teach.pos	196	4.980	5.0	0	11	2.134	0.152	0.301	3.00	YES	0.405	-0.068
Experimental				8 ano		vocab.teach.pos	181	5.210	5.0	1	11	2.188	0.163	0.321	3.00	YES	0.356	-0.343
Experimental				9 ano		vocab.teach.pos	171	6.316	6.0	0	12	2.211	0.169	0.334	3.00	YES	-0.202	0.117
Controle					1st quintile	vocab.teach.pre	102	1.618	2.0	0	2	0.598	0.059	0.117	1.00	few data	0.000	0.000
Controle					2nd quintile	vocab.teach.pre	80	3.000	3.0	3	3	0.000	0.000	0.000	0.00	few data	0.000	0.000
Controle					3rd quintile	vocab.teach.pre	172	4.506	5.0	4	5	0.501	0.038	0.075	1.00	few data	0.000	0.000
Controle					4th quintile	vocab.teach.pre	64	6.000	6.0	6	6	0.000	0.000	0.000	0.00	few data	0.000	0.000
Controle					5th quintile	vocab.teach.pre	97	7.794	7.0	7	13	1.060	0.108	0.214	1.00	NO	1.866	5.018
Experimental					1st quintile	vocab.teach.pre	93	1.710	2.0	0	2	0.480	0.050	0.099	1.00	few data	0.000	0.000
Experimental					2nd quintile	vocab.teach.pre	105	3.000	3.0	3	3	0.000	0.000	0.000	0.00	few data	0.000	0.000
Experimental					3rd quintile	vocab.teach.pre	273	4.451	4.0	4	5	0.498	0.030	0.059	1.00	few data	0.000	0.000
Experimental					4th quintile	vocab.teach.pre	99	6.000	6.0	6	6	0.000	0.000	0.000	0.00	few data	0.000	0.000
Experimental					5th quintile	vocab.teach.pre	143	7.762	7.0	7	13	1.034	0.086	0.171	1.00	NO	1.809	4.529
Controle					1st quintile	vocab.teach.pos	102	3.431	3.0	0	10	1.927	0.191	0.378	2.75	YES	0.412	0.170
Controle					2nd quintile	vocab.teach.pos	80	4.138	4.0	0	9	1.674	0.187	0.373	2.00	YES	0.265	0.205
Controle					3rd quintile	vocab.teach.pos	172	4.634	5.0	0	10	1.895	0.144	0.285	3.00	YES	0.104	-0.188
Controle					4th quintile	vocab.teach.pos	64	5.922	6.0	0	10	2.125	0.266	0.531	2.25	NO	-0.573	-0.201
Controle					5th quintile	vocab.teach.pos	97	6.866	7.0	2	11	2.024	0.206	0.408	2.00	YES	-0.245	-0.452
Experimental					1st quintile	vocab.teach.pos	93	3.871	3.0	1	10	1.946	0.202	0.401	2.00	NO	0.617	-0.028
Experimental					2nd quintile	vocab.teach.pos	105	4.686	5.0	1	9	1.723	0.168	0.333	3.00	YES	0.107	-0.648
Experimental					3rd quintile	vocab.teach.pos	273	5.154	5.0	0	12	2.058	0.125	0.245	3.00	YES	0.228	-0.010
Experimental					4th quintile	vocab.teach.pos	99	5.778	6.0	0	11	2.183	0.219	0.435	3.00	YES	-0.155	-0.146
Experimental					5th quintile	vocab.teach.pos	143	7.133	7.0	1	12	2.173	0.182	0.359	3.00	YES	-0.276	-0.418

ANCOVA Table Comparison

df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]

	Effect	DFn	DFd	F	p	p<.05	ges	DFn’	DFd’	F’	p’	p<.05’	ges’
1	grupo	1	1225	10.282	0.001	*	0.008	1	1226	9.066	0.003	*	0.007
2	vocab.teach.pre	1	1225	380.801	0.000	*	0.237	1	1226	374.895	0.000	*	0.234
4	grupo:Sexo	1	1223	3.710	0.054		0.003	1	1224	3.045	0.081		0.002
5	Sexo	1	1223	13.356	0.000	*	0.011	1	1224	14.128	0.000	*	0.011
8	grupo:Zona	1	844	0.323	0.570		0.000	1	845	0.479	0.489		0.001
10	Zona	1	844	0.020	0.887		0.000	1	845	0.063	0.802		0.000
11	Cor.Raca	2	509	0.773	0.462		0.003	2	509	0.773	0.462		0.003
13	grupo:Cor.Raca	2	509	0.683	0.506		0.003	2	509	0.683	0.506		0.003
16	grupo:Serie	3	1219	3.860	0.009	*	0.009	3	1220	3.564	0.014	*	0.009
17	Serie	3	1219	11.129	0.000	*	0.027	3	1220	11.904	0.000	*	0.028
20	grupo:vocab.teach.quintile	4	1217	0.986	0.414		0.003	4	1218	0.853	0.491		0.003
22	vocab.teach.quintile	4	1217	1.750	0.137		0.006	4	1218	1.465	0.210		0.005

PairWise Table Comparison

df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
            names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]

grupo	Sexo	Zona	Cor.Raca	Serie	vocab.teach.quintile	group1	group2	df	statistic	p	p.adj	p.adj.signif	df’	statistic’	p’	p.adj’	p.adj.signif’
Controle						pre	pos	2452	-2.909	0.004	0.004	**	2454	-3.037	0.002	0.002	**
Experimental						pre	pos	2452	-5.577	0.000	0.000	****	2454	-5.555	0.000	0.000	****
						Controle	Experimental	1225	-3.207	0.001	0.001	**	1226	-3.011	0.003	0.003	**
Controle	F					pre	pos	2448	-2.273	0.023	0.023	*	2450	-2.462	0.014	0.014	*
Controle	M					pre	pos	2448	-1.851	0.064	0.064	ns	2450	-1.843	0.065	0.065	ns
Controle						F	M	1223	0.900	0.368	0.368	ns	1224	1.107	0.268	0.268	ns
Experimental	F					pre	pos	2448	-5.761	0.000	0.000	****	2450	-5.739	0.000	0.000	****
Experimental	M					pre	pos	2448	-2.089	0.037	0.037	*	2450	-2.081	0.038	0.038	*
Experimental						F	M	1223	4.032	0.000	0.000	****	1224	3.994	0.000	0.000	****
	F					Controle	Experimental	1223	-3.615	0.000	0.000	***	1224	-3.350	0.001	0.001	***
	M					Controle	Experimental	1223	-0.859	0.391	0.391	ns	1224	-0.849	0.396	0.396	ns
Controle						Rural	Urbana	844	0.528	0.598	0.598	ns	845	0.693	0.488	0.488	ns
Controle		Rural				pre	pos	1690	-2.672	0.008	0.008	**	1692	-2.856	0.004	0.004	**
Controle		Urbana				pre	pos	1690	-1.467	0.143	0.143	ns	1692	-1.459	0.145	0.145	ns
Experimental						Rural	Urbana	844	-0.253	0.801	0.801	ns	845	-0.248	0.804	0.804	ns
Experimental		Rural				pre	pos	1690	-3.806	0.000	0.000	***	1692	-3.785	0.000	0.000	***
Experimental		Urbana				pre	pos	1690	-3.015	0.003	0.003	**	1692	-2.997	0.003	0.003	**
		Rural				Controle	Experimental	844	-1.605	0.109	0.109	ns	845	-1.357	0.175	0.175	ns
		Urbana				Controle	Experimental	844	-1.862	0.063	0.063	ns	845	-1.834	0.067	0.067	ns
Controle			Branca			pre	pos	1020	-1.272	0.204	0.204	ns	1020	-1.272	0.204	0.204	ns
Controle			Indígena			pre	pos	1020	-1.520	0.129	0.129	ns	1020	-1.520	0.129	0.129	ns
Controle						Indígena	Branca	509	1.438	0.151	0.453	ns	509	1.438	0.151	0.453	ns
Controle						Parda	Branca	509	0.177	0.860	1.000	ns	509	0.177	0.860	1.000	ns
Controle						Parda	Indígena	509	-1.448	0.148	0.445	ns	509	-1.448	0.148	0.445	ns
Controle			Parda			pre	pos	1020	-2.288	0.022	0.022	*	1020	-2.288	0.022	0.022	*
Experimental			Branca			pre	pos	1020	-0.982	0.326	0.326	ns	1020	-0.982	0.326	0.326	ns
Experimental			Indígena			pre	pos	1020	-1.292	0.197	0.197	ns	1020	-1.292	0.197	0.197	ns
Experimental						Indígena	Branca	509	0.396	0.692	1.000	ns	509	0.396	0.692	1.000	ns
Experimental						Parda	Branca	509	0.819	0.413	1.000	ns	509	0.819	0.413	1.000	ns
Experimental						Parda	Indígena	509	0.053	0.958	1.000	ns	509	0.053	0.958	1.000	ns
Experimental			Parda			pre	pos	1020	-3.842	0.000	0.000	***	1020	-3.842	0.000	0.000	***
			Branca			Controle	Experimental	509	-0.324	0.746	0.746	ns	509	-0.324	0.746	0.746	ns
			Indígena			Controle	Experimental	509	0.763	0.446	0.446	ns	509	0.763	0.446	0.446	ns
			Parda			Controle	Experimental	509	-1.455	0.146	0.146	ns	509	-1.455	0.146	0.146	ns
Controle				6 ano		pre	pos	2440	-0.946	0.344	0.344	ns	2442	-0.942	0.346	0.346	ns
Controle				7 ano		pre	pos	2440	-2.671	0.008	0.008	**	2442	-2.662	0.008	0.008	**
Controle				8 ano		pre	pos	2440	-1.714	0.087	0.087	ns	2442	-1.708	0.088	0.088	ns
Controle				9 ano		pre	pos	2440	-0.621	0.534	0.534	ns	2442	-0.925	0.355	0.355	ns
Controle						6 ano	7 ano	1219	-2.021	0.044	0.261	ns	1220	-2.003	0.045	0.272	ns
Controle						6 ano	8 ano	1219	-2.347	0.019	0.115	ns	1220	-2.329	0.020	0.120	ns
Controle						6 ano	9 ano	1219	-3.582	0.000	0.002	**	1220	-3.890	0.000	0.001	***
Controle						7 ano	8 ano	1219	-0.552	0.581	1.000	ns	1220	-0.550	0.582	1.000	ns
Controle						7 ano	9 ano	1219	-1.756	0.079	0.476	ns	1220	-2.080	0.038	0.227	ns
Controle						8 ano	9 ano	1219	-1.088	0.277	1.000	ns	1220	-1.384	0.167	1.000	ns
Experimental				6 ano		pre	pos	2440	-3.924	0.000	0.000	****	2442	-3.911	0.000	0.000	****
Experimental				7 ano		pre	pos	2440	-1.104	0.270	0.270	ns	2442	-1.101	0.271	0.271	ns
Experimental				8 ano		pre	pos	2440	-1.873	0.061	0.061	ns	2442	-1.867	0.062	0.062	ns
Experimental				9 ano		pre	pos	2440	-4.806	0.000	0.000	****	2442	-4.790	0.000	0.000	****
Experimental						6 ano	7 ano	1219	2.081	0.038	0.226	ns	1220	2.059	0.040	0.238	ns
Experimental						6 ano	8 ano	1219	1.067	0.286	1.000	ns	1220	1.053	0.292	1.000	ns
Experimental						6 ano	9 ano	1219	-3.119	0.002	0.011	*	1220	-3.098	0.002	0.012	*
Experimental						7 ano	8 ano	1219	-1.021	0.307	1.000	ns	1220	-1.012	0.312	1.000	ns
Experimental						7 ano	9 ano	1219	-5.373	0.000	0.000	****	1220	-5.329	0.000	0.000	****
Experimental						8 ano	9 ano	1219	-4.290	0.000	0.000	***	1220	-4.254	0.000	0.000	***
				6 ano		Controle	Experimental	1219	-3.797	0.000	0.000	***	1220	-3.766	0.000	0.000	***
				7 ano		Controle	Experimental	1219	0.209	0.834	0.834	ns	1220	0.202	0.840	0.840	ns
				8 ano		Controle	Experimental	1219	-0.091	0.927	0.927	ns	1220	-0.092	0.927	0.927	ns
				9 ano		Controle	Experimental	1219	-2.661	0.008	0.008	**	1220	-2.302	0.022	0.022	*
Controle					1st quintile	pre	pos	2436	-8.813	0.000	0.000	****	2438	-8.728	0.000	0.000	****
Controle					2nd quintile	pre	pos	2436	-4.895	0.000	0.000	****	2438	-4.848	0.000	0.000	****
Controle					3rd quintile	pre	pos	2436	-0.807	0.420	0.420	ns	2438	-1.159	0.246	0.246	ns
Controle					4th quintile	pre	pos	2436	0.301	0.764	0.764	ns	2438	0.298	0.766	0.766	ns
Controle					5th quintile	pre	pos	2436	4.396	0.000	0.000	****	2438	4.354	0.000	0.000	****
Controle						1st quintile	2nd quintile	1217	-0.313	0.754	1.000	ns	1218	-0.261	0.794	1.000	ns
Controle						1st quintile	3rd quintile	1217	0.166	0.868	1.000	ns	1218	0.098	0.922	1.000	ns
Controle						1st quintile	4th quintile	1217	-1.093	0.275	1.000	ns	1218	-0.986	0.325	1.000	ns
Controle						1st quintile	5th quintile	1217	-1.120	0.263	1.000	ns	1218	-1.000	0.317	1.000	ns
Controle						2nd quintile	3rd quintile	1217	0.536	0.592	1.000	ns	1218	0.397	0.692	1.000	ns
Controle						2nd quintile	4th quintile	1217	-1.076	0.282	1.000	ns	1218	-0.986	0.324	1.000	ns
Controle						2nd quintile	5th quintile	1217	-1.154	0.249	1.000	ns	1218	-1.041	0.298	1.000	ns
Controle						3rd quintile	4th quintile	1217	-1.966	0.050	0.495	ns	1218	-1.714	0.087	0.867	ns
Controle						3rd quintile	5th quintile	1217	-1.973	0.049	0.487	ns	1218	-1.716	0.086	0.863	ns
Controle						4th quintile	5th quintile	1217	-0.440	0.660	1.000	ns	1218	-0.379	0.705	1.000	ns
Experimental					1st quintile	pre	pos	2436	-10.028	0.000	0.000	****	2438	-9.931	0.000	0.000	****
Experimental					2nd quintile	pre	pos	2436	-8.310	0.000	0.000	****	2438	-8.230	0.000	0.000	****
Experimental					3rd quintile	pre	pos	2436	-5.591	0.000	0.000	****	2438	-5.537	0.000	0.000	****
Experimental					4th quintile	pre	pos	2436	1.064	0.288	0.288	ns	2438	1.054	0.292	0.292	ns
Experimental					5th quintile	pre	pos	2436	3.621	0.000	0.000	***	2438	3.586	0.000	0.000	***
Experimental						1st quintile	2nd quintile	1217	-0.814	0.416	1.000	ns	1218	-0.757	0.449	1.000	ns
Experimental						1st quintile	3rd quintile	1217	-0.236	0.813	1.000	ns	1218	-0.144	0.885	1.000	ns
Experimental						1st quintile	4th quintile	1217	-0.059	0.953	1.000	ns	1218	0.041	0.967	1.000	ns
Experimental						1st quintile	5th quintile	1217	-0.965	0.335	1.000	ns	1218	-0.846	0.398	1.000	ns
Experimental						2nd quintile	3rd quintile	1217	0.627	0.531	1.000	ns	1218	0.683	0.495	1.000	ns
Experimental						2nd quintile	4th quintile	1217	0.554	0.580	1.000	ns	1218	0.635	0.526	1.000	ns
Experimental						2nd quintile	5th quintile	1217	-0.697	0.486	1.000	ns	1218	-0.585	0.559	1.000	ns
Experimental						3rd quintile	4th quintile	1217	0.197	0.844	1.000	ns	1218	0.260	0.795	1.000	ns
Experimental						3rd quintile	5th quintile	1217	-1.407	0.160	1.000	ns	1218	-1.292	0.197	1.000	ns
Experimental						4th quintile	5th quintile	1217	-1.893	0.059	0.585	ns	1218	-1.808	0.071	0.708	ns
					1st quintile	Controle	Experimental	1217	-1.409	0.159	0.159	ns	1218	-1.391	0.165	0.165	ns
					2nd quintile	Controle	Experimental	1217	-1.869	0.062	0.062	ns	1218	-1.850	0.065	0.065	ns
					3rd quintile	Controle	Experimental	1217	-2.828	0.005	0.005	**	1218	-2.506	0.012	0.012	*
					4th quintile	Controle	Experimental	1217	0.455	0.649	0.649	ns	1218	0.450	0.653	0.653	ns
					5th quintile	Controle	Experimental	1217	-1.080	0.280	0.280	ns	1218	-1.070	0.285	0.285	ns

EMMS Table Comparison

df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
            names(df)[!names(df) %in% names(lfatores)])]

grupo	Sexo	Zona	Cor.Raca	Serie	vocab.teach.quintile	N	M (pre)	SE (pre)	M (unadj)	SE (unadj)	M (adj)	SE (adj)	conf.low	conf.high	N’	M (pre)’	SE (pre)’	M (unadj)’	SE (unadj)’	M (adj)’	SE (adj)’	conf.low’	conf.high’	N-N’
Controle						515	4.505	0.095	4.899	0.099	4.977	0.087	4.806	5.148	516	4.506	0.095	4.919	0.101	4.996	0.088	4.824	5.169	-1
Experimental						713	4.759	0.076	5.401	0.085	5.345	0.074	5.199	5.490	713	4.759	0.076	5.401	0.085	5.345	0.075	5.198	5.492	0
Controle	F					258	4.585	0.127	5.019	0.138	5.055	0.122	4.815	5.295	259	4.587	0.126	5.058	0.143	5.093	0.123	4.851	5.335	-1
Controle	M					257	4.424	0.143	4.778	0.142	4.899	0.123	4.658	5.140	257	4.424	0.143	4.778	0.142	4.899	0.124	4.656	5.142	0
Experimental	F					368	4.777	0.108	5.698	0.117	5.632	0.103	5.431	5.834	368	4.777	0.108	5.698	0.117	5.632	0.104	5.429	5.836	0
Experimental	M					345	4.739	0.106	5.084	0.122	5.038	0.106	4.830	5.246	345	4.739	0.106	5.084	0.122	5.038	0.107	4.828	5.248	0
Controle		Rural				252	4.460	0.131	4.976	0.145	5.061	0.123	4.819	5.302	253	4.462	0.130	5.016	0.150	5.100	0.125	4.855	5.344	-1
Controle		Urbana				114	4.412	0.184	4.833	0.192	4.944	0.183	4.585	5.303	114	4.412	0.184	4.833	0.192	4.945	0.186	4.581	5.309	0
Experimental		Rural				294	4.694	0.121	5.374	0.138	5.330	0.114	5.106	5.554	294	4.694	0.121	5.374	0.138	5.330	0.115	5.103	5.557	0
Experimental		Urbana				189	4.815	0.155	5.487	0.157	5.376	0.142	5.097	5.655	189	4.815	0.155	5.487	0.157	5.376	0.144	5.093	5.659	0
Controle			Branca			54	4.352	0.308	4.889	0.328	4.983	0.270	4.454	5.513	54	4.352	0.308	4.889	0.328	4.983	0.270	4.454	5.513	0
Controle			Indígena			13	4.615	0.460	5.923	0.582	5.864	0.549	4.784	6.943	13	4.615	0.460	5.923	0.582	5.864	0.549	4.784	6.943	0
Controle			Parda			168	4.458	0.167	5.006	0.175	5.038	0.153	4.738	5.338	168	4.458	0.167	5.006	0.175	5.038	0.153	4.738	5.338	0
Experimental			Branca			62	5.000	0.224	5.387	0.297	5.103	0.252	4.607	5.599	62	5.000	0.224	5.387	0.297	5.103	0.252	4.607	5.599	0
Experimental			Indígena			18	4.167	0.390	5.111	0.478	5.314	0.467	4.396	6.231	18	4.167	0.390	5.111	0.478	5.314	0.467	4.396	6.231	0
Experimental			Parda			201	4.478	0.145	5.318	0.167	5.339	0.140	5.065	5.614	201	4.478	0.145	5.318	0.167	5.339	0.140	5.065	5.614	0
Controle				6 ano		146	3.925	0.157	4.158	0.162	4.521	0.162	4.203	4.840	146	3.925	0.157	4.158	0.162	4.520	0.164	4.199	4.842	0
Controle				7 ano		152	4.013	0.156	4.658	0.165	4.978	0.159	4.666	5.289	152	4.013	0.156	4.658	0.165	4.977	0.160	4.662	5.291	0
Controle				8 ano		100	4.560	0.230	5.070	0.251	5.116	0.195	4.734	5.498	100	4.560	0.230	5.070	0.251	5.116	0.197	4.731	5.502	0
Controle				9 ano		117	5.821	0.196	5.991	0.205	5.407	0.183	5.048	5.766	118	5.814	0.194	6.068	0.217	5.489	0.184	5.128	5.850	-1
Experimental				6 ano		165	4.255	0.148	5.164	0.183	5.363	0.152	5.064	5.661	165	4.255	0.148	5.164	0.183	5.362	0.153	5.061	5.663	0
Experimental				7 ano		196	4.745	0.154	4.980	0.152	4.933	0.139	4.660	5.206	196	4.745	0.154	4.980	0.152	4.934	0.140	4.658	5.209	0
Experimental				8 ano		181	4.796	0.145	5.210	0.163	5.138	0.145	4.854	5.422	181	4.796	0.145	5.210	0.163	5.139	0.146	4.852	5.425	0
Experimental				9 ano		171	5.222	0.147	6.316	0.169	6.031	0.150	5.737	6.325	171	5.222	0.147	6.316	0.169	6.032	0.151	5.735	6.328	0
Controle					1st quintile	102	1.618	0.059	3.431	0.191	4.759	0.350	4.073	5.446	102	1.618	0.059	3.431	0.191	4.795	0.353	4.101	5.488	0
Controle					2nd quintile	80	3.000	0.000	4.138	0.187	4.861	0.272	4.328	5.393	80	3.000	0.000	4.138	0.187	4.880	0.274	4.342	5.418	0
Controle					3rd quintile	172	4.506	0.038	4.634	0.144	4.698	0.151	4.401	4.995	173	4.509	0.038	4.694	0.156	4.758	0.152	4.459	5.057	-1
Controle					4th quintile	64	6.000	0.000	5.922	0.266	5.332	0.279	4.786	5.879	64	6.000	0.000	5.922	0.266	5.317	0.281	4.764	5.869	0
Controle					5th quintile	97	7.794	0.108	6.866	0.206	5.491	0.361	4.783	6.200	97	7.794	0.108	6.866	0.206	5.455	0.365	4.739	6.170	0
Experimental					1st quintile	93	1.710	0.050	3.871	0.202	5.159	0.348	4.476	5.841	93	1.710	0.050	3.871	0.202	5.193	0.351	4.503	5.883	0
Experimental					2nd quintile	105	3.000	0.000	4.686	0.168	5.409	0.249	4.920	5.898	105	3.000	0.000	4.686	0.168	5.428	0.252	4.934	5.922	0
Experimental					3rd quintile	273	4.451	0.030	5.154	0.125	5.242	0.121	5.004	5.480	273	4.451	0.030	5.154	0.125	5.245	0.122	5.004	5.485	0
Experimental					4th quintile	99	6.000	0.000	5.778	0.219	5.188	0.237	4.724	5.652	99	6.000	0.000	5.778	0.219	5.172	0.239	4.703	5.642	0
Experimental					5th quintile	143	7.762	0.086	7.133	0.182	5.772	0.340	5.105	6.439	143	7.762	0.086	7.133	0.182	5.736	0.343	5.062	6.410	0

CONSORT tables

Enrolment participants table

df <- do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
  tdat2 <- tdat
  tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared" 
  count_(group_by(tdat2, grupo), x)
}))

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

grupo	Sexo	n	Zona	Cor.Raca	Serie	Idade
Controle	F	376
Controle	M	393
Experimental	F	508
Experimental	M	528
Controle		211	Not declared
Controle		351	Rural
Controle		207	Urbana
Experimental		321	Not declared
Experimental		426	Rural
Experimental		289	Urbana
Controle		77		Branca
Controle		21		Indígena
Controle		416		Not declared
Controle		254		Parda
Controle		1		Preta
Experimental		3		Amarela
Experimental		92		Branca
Experimental		22		Indígena
Experimental		619		Not declared
Experimental		299		Parda
Experimental		1		Preta
Controle		215			6 ano
Controle		214			7 ano
Controle		163			8 ano
Controle		177			9 ano
Experimental		250			6 ano
Experimental		283			7 ano
Experimental		258			8 ano
Experimental		245			9 ano
Controle		112				11
Controle		162				12
Controle		183				13
Controle		172				14
Controle		90				15
Controle		30				16
Controle		9				17
Controle		5				18
Controle		1				19
Controle		2				20
Controle		1				21
Controle		1				22
Controle		1				34
Experimental		1				1
Experimental		160				11
Experimental		215				12
Experimental		226				13
Experimental		220				14
Experimental		126				15
Experimental		52				16
Experimental		21				17
Experimental		7				18
Experimental		3				19
Experimental		1				20
Experimental		1				21
Experimental		1				23
Experimental		1				4
Experimental		1				9

Allocated participants table (after removed students with disabilities/special needs)

df <- do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
  tdat2 <- gdat
  tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared" 
  count_(group_by(tdat2, grupo), x)
}))

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

grupo	Sexo	n	Zona	Cor.Raca	Serie	Idade
Controle	F	367
Controle	M	386
Experimental	F	502
Experimental	M	515
Controle		208	Not declared
Controle		343	Rural
Controle		202	Urbana
Experimental		315	Not declared
Experimental		421	Rural
Experimental		281	Urbana
Controle		76		Branca
Controle		20		Indígena
Controle		404		Not declared
Controle		252		Parda
Controle		1		Preta
Experimental		2		Amarela
Experimental		89		Branca
Experimental		22		Indígena
Experimental		610		Not declared
Experimental		293		Parda
Experimental		1		Preta
Controle		213			6 ano
Controle		209			7 ano
Controle		158			8 ano
Controle		173			9 ano
Experimental		245			6 ano
Experimental		277			7 ano
Experimental		253			8 ano
Experimental		242			9 ano
Controle		112				11
Controle		161				12
Controle		181				13
Controle		166				14
Controle		87				15
Controle		30				16
Controle		7				17
Controle		5				18
Controle		1				20
Controle		1				21
Controle		1				22
Controle		1				34
Experimental		1				1
Experimental		157				11
Experimental		214				12
Experimental		220				13
Experimental		218				14
Experimental		124				15
Experimental		50				16
Experimental		19				17
Experimental		6				18
Experimental		3				19
Experimental		1				20
Experimental		1				21
Experimental		1				23
Experimental		1				4
Experimental		1				9

Follow-up participants table (after removed students with does-not have pre and post-test)

df <- do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
  tdat2 <- gdat[gdat$id %in% dat$id,]  
  tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared" 
  count_(group_by(tdat2, grupo), x)
}))

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

grupo	Sexo	n	Zona	Cor.Raca	Serie	Idade
Controle	F	259
Controle	M	257
Experimental	F	368
Experimental	M	345
Controle		149	Not declared
Controle		253	Rural
Controle		114	Urbana
Experimental		230	Not declared
Experimental		294	Rural
Experimental		189	Urbana
Controle		54		Branca
Controle		13		Indígena
Controle		280		Not declared
Controle		168		Parda
Controle		1		Preta
Experimental		1		Amarela
Experimental		62		Branca
Experimental		18		Indígena
Experimental		430		Not declared
Experimental		201		Parda
Experimental		1		Preta
Controle		146			6 ano
Controle		152			7 ano
Controle		100			8 ano
Controle		118			9 ano
Experimental		165			6 ano
Experimental		196			7 ano
Experimental		181			8 ano
Experimental		171			9 ano
Controle		88				11
Controle		122				12
Controle		125				13
Controle		113				14
Controle		43				15
Controle		17				16
Controle		5				17
Controle		2				18
Controle		1				34
Experimental		119				11
Experimental		155				12
Experimental		159				13
Experimental		155				14
Experimental		80				15
Experimental		27				16
Experimental		9				17
Experimental		3				18
Experimental		1				19
Experimental		1				20
Experimental		1				21
Experimental		1				23
Experimental		1				4
Experimental		1				9

Participants table for Analysis (after exclude non-normal data)

df <- do.call(rbind, lapply(names(ldat), FUN = function(tname) {
  dat2 <- ldat[[tname]]
  data.frame(
    "For Analysis of ANCOVA" = tname,
    do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
      tdat2 <- gdat[gdat$id %in% dat2$id,]  
      tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared" 
      count_(group_by(tdat2, grupo), x)
  })))
}))  

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

For.Analysis.of.ANCOVA	grupo	Sexo	n	Zona	Cor.Raca	Serie	Idade
grupo	Controle	F	258
grupo	Controle	M	257
grupo	Experimental	F	368
grupo	Experimental	M	345
grupo	Controle		149	Not declared
grupo	Controle		252	Rural
grupo	Controle		114	Urbana
grupo	Experimental		230	Not declared
grupo	Experimental		294	Rural
grupo	Experimental		189	Urbana
grupo	Controle		54		Branca
grupo	Controle		13		Indígena
grupo	Controle		279		Not declared
grupo	Controle		168		Parda
grupo	Controle		1		Preta
grupo	Experimental		1		Amarela
grupo	Experimental		62		Branca
grupo	Experimental		18		Indígena
grupo	Experimental		430		Not declared
grupo	Experimental		201		Parda
grupo	Experimental		1		Preta
grupo	Controle		146			6 ano
grupo	Controle		152			7 ano
grupo	Controle		100			8 ano
grupo	Controle		117			9 ano
grupo	Experimental		165			6 ano
grupo	Experimental		196			7 ano
grupo	Experimental		181			8 ano
grupo	Experimental		171			9 ano
grupo	Controle		88				11
grupo	Controle		122				12
grupo	Controle		125				13
grupo	Controle		113				14
grupo	Controle		43				15
grupo	Controle		17				16
grupo	Controle		5				17
grupo	Controle		1				18
grupo	Controle		1				34
grupo	Experimental		119				11
grupo	Experimental		155				12
grupo	Experimental		159				13
grupo	Experimental		155				14
grupo	Experimental		80				15
grupo	Experimental		27				16
grupo	Experimental		9				17
grupo	Experimental		3				18
grupo	Experimental		1				19
grupo	Experimental		1				20
grupo	Experimental		1				21
grupo	Experimental		1				23
grupo	Experimental		1				4
grupo	Experimental		1				9
grupo:Sexo	Controle	F	258
grupo:Sexo	Controle	M	257
grupo:Sexo	Experimental	F	368
grupo:Sexo	Experimental	M	345
grupo:Sexo	Controle		149	Not declared
grupo:Sexo	Controle		252	Rural
grupo:Sexo	Controle		114	Urbana
grupo:Sexo	Experimental		230	Not declared
grupo:Sexo	Experimental		294	Rural
grupo:Sexo	Experimental		189	Urbana
grupo:Sexo	Controle		54		Branca
grupo:Sexo	Controle		13		Indígena
grupo:Sexo	Controle		279		Not declared
grupo:Sexo	Controle		168		Parda
grupo:Sexo	Controle		1		Preta
grupo:Sexo	Experimental		1		Amarela
grupo:Sexo	Experimental		62		Branca
grupo:Sexo	Experimental		18		Indígena
grupo:Sexo	Experimental		430		Not declared
grupo:Sexo	Experimental		201		Parda
grupo:Sexo	Experimental		1		Preta
grupo:Sexo	Controle		146			6 ano
grupo:Sexo	Controle		152			7 ano
grupo:Sexo	Controle		100			8 ano
grupo:Sexo	Controle		117			9 ano
grupo:Sexo	Experimental		165			6 ano
grupo:Sexo	Experimental		196			7 ano
grupo:Sexo	Experimental		181			8 ano
grupo:Sexo	Experimental		171			9 ano
grupo:Sexo	Controle		88				11
grupo:Sexo	Controle		122				12
grupo:Sexo	Controle		125				13
grupo:Sexo	Controle		113				14
grupo:Sexo	Controle		43				15
grupo:Sexo	Controle		17				16
grupo:Sexo	Controle		5				17
grupo:Sexo	Controle		1				18
grupo:Sexo	Controle		1				34
grupo:Sexo	Experimental		119				11
grupo:Sexo	Experimental		155				12
grupo:Sexo	Experimental		159				13
grupo:Sexo	Experimental		155				14
grupo:Sexo	Experimental		80				15
grupo:Sexo	Experimental		27				16
grupo:Sexo	Experimental		9				17
grupo:Sexo	Experimental		3				18
grupo:Sexo	Experimental		1				19
grupo:Sexo	Experimental		1				20
grupo:Sexo	Experimental		1				21
grupo:Sexo	Experimental		1				23
grupo:Sexo	Experimental		1				4
grupo:Sexo	Experimental		1				9
grupo:Zona	Controle	F	183
grupo:Zona	Controle	M	183
grupo:Zona	Experimental	F	241
grupo:Zona	Experimental	M	242
grupo:Zona	Controle		252	Rural
grupo:Zona	Controle		114	Urbana
grupo:Zona	Experimental		294	Rural
grupo:Zona	Experimental		189	Urbana
grupo:Zona	Controle		44		Branca
grupo:Zona	Controle		12		Indígena
grupo:Zona	Controle		178		Not declared
grupo:Zona	Controle		131		Parda
grupo:Zona	Controle		1		Preta
grupo:Zona	Experimental		49		Branca
grupo:Zona	Experimental		17		Indígena
grupo:Zona	Experimental		250		Not declared
grupo:Zona	Experimental		166		Parda
grupo:Zona	Experimental		1		Preta
grupo:Zona	Controle		125			6 ano
grupo:Zona	Controle		127			7 ano
grupo:Zona	Controle		56			8 ano
grupo:Zona	Controle		58			9 ano
grupo:Zona	Experimental		123			6 ano
grupo:Zona	Experimental		167			7 ano
grupo:Zona	Experimental		89			8 ano
grupo:Zona	Experimental		104			9 ano
grupo:Zona	Controle		76				11
grupo:Zona	Controle		108				12
grupo:Zona	Controle		82				13
grupo:Zona	Controle		59				14
grupo:Zona	Controle		27				15
grupo:Zona	Controle		10				16
grupo:Zona	Controle		2				17
grupo:Zona	Controle		1				18
grupo:Zona	Controle		1				34
grupo:Zona	Experimental		89				11
grupo:Zona	Experimental		136				12
grupo:Zona	Experimental		93				13
grupo:Zona	Experimental		84				14
grupo:Zona	Experimental		53				15
grupo:Zona	Experimental		14				16
grupo:Zona	Experimental		8				17
grupo:Zona	Experimental		1				18
grupo:Zona	Experimental		1				19
grupo:Zona	Experimental		1				20
grupo:Zona	Experimental		1				21
grupo:Zona	Experimental		1				23
grupo:Zona	Experimental		1				9
grupo:Cor.Raca	Controle	F	119
grupo:Cor.Raca	Controle	M	116
grupo:Cor.Raca	Experimental	F	138
grupo:Cor.Raca	Experimental	M	143
grupo:Cor.Raca	Controle		48	Not declared
grupo:Cor.Raca	Controle		145	Rural
grupo:Cor.Raca	Controle		42	Urbana
grupo:Cor.Raca	Experimental		49	Not declared
grupo:Cor.Raca	Experimental		185	Rural
grupo:Cor.Raca	Experimental		47	Urbana
grupo:Cor.Raca	Controle		54		Branca
grupo:Cor.Raca	Controle		13		Indígena
grupo:Cor.Raca	Controle		168		Parda
grupo:Cor.Raca	Experimental		62		Branca
grupo:Cor.Raca	Experimental		18		Indígena
grupo:Cor.Raca	Experimental		201		Parda
grupo:Cor.Raca	Controle		66			6 ano
grupo:Cor.Raca	Controle		74			7 ano
grupo:Cor.Raca	Controle		42			8 ano
grupo:Cor.Raca	Controle		53			9 ano
grupo:Cor.Raca	Experimental		75			6 ano
grupo:Cor.Raca	Experimental		66			7 ano
grupo:Cor.Raca	Experimental		72			8 ano
grupo:Cor.Raca	Experimental		68			9 ano
grupo:Cor.Raca	Controle		48				11
grupo:Cor.Raca	Controle		53				12
grupo:Cor.Raca	Controle		58				13
grupo:Cor.Raca	Controle		54				14
grupo:Cor.Raca	Controle		12				15
grupo:Cor.Raca	Controle		7				16
grupo:Cor.Raca	Controle		3				17
grupo:Cor.Raca	Experimental		57				11
grupo:Cor.Raca	Experimental		49				12
grupo:Cor.Raca	Experimental		55				13
grupo:Cor.Raca	Experimental		61				14
grupo:Cor.Raca	Experimental		35				15
grupo:Cor.Raca	Experimental		17				16
grupo:Cor.Raca	Experimental		4				17
grupo:Cor.Raca	Experimental		1				18
grupo:Cor.Raca	Experimental		1				21
grupo:Cor.Raca	Experimental		1				23
grupo:Serie	Controle	F	258
grupo:Serie	Controle	M	257
grupo:Serie	Experimental	F	368
grupo:Serie	Experimental	M	345
grupo:Serie	Controle		149	Not declared
grupo:Serie	Controle		252	Rural
grupo:Serie	Controle		114	Urbana
grupo:Serie	Experimental		230	Not declared
grupo:Serie	Experimental		294	Rural
grupo:Serie	Experimental		189	Urbana
grupo:Serie	Controle		54		Branca
grupo:Serie	Controle		13		Indígena
grupo:Serie	Controle		279		Not declared
grupo:Serie	Controle		168		Parda
grupo:Serie	Controle		1		Preta
grupo:Serie	Experimental		1		Amarela
grupo:Serie	Experimental		62		Branca
grupo:Serie	Experimental		18		Indígena
grupo:Serie	Experimental		430		Not declared
grupo:Serie	Experimental		201		Parda
grupo:Serie	Experimental		1		Preta
grupo:Serie	Controle		146			6 ano
grupo:Serie	Controle		152			7 ano
grupo:Serie	Controle		100			8 ano
grupo:Serie	Controle		117			9 ano
grupo:Serie	Experimental		165			6 ano
grupo:Serie	Experimental		196			7 ano
grupo:Serie	Experimental		181			8 ano
grupo:Serie	Experimental		171			9 ano
grupo:Serie	Controle		88				11
grupo:Serie	Controle		122				12
grupo:Serie	Controle		125				13
grupo:Serie	Controle		113				14
grupo:Serie	Controle		43				15
grupo:Serie	Controle		17				16
grupo:Serie	Controle		5				17
grupo:Serie	Controle		1				18
grupo:Serie	Controle		1				34
grupo:Serie	Experimental		119				11
grupo:Serie	Experimental		155				12
grupo:Serie	Experimental		159				13
grupo:Serie	Experimental		155				14
grupo:Serie	Experimental		80				15
grupo:Serie	Experimental		27				16
grupo:Serie	Experimental		9				17
grupo:Serie	Experimental		3				18
grupo:Serie	Experimental		1				19
grupo:Serie	Experimental		1				20
grupo:Serie	Experimental		1				21
grupo:Serie	Experimental		1				23
grupo:Serie	Experimental		1				4
grupo:Serie	Experimental		1				9
grupo:vocab.teach.quintile	Controle	F	258
grupo:vocab.teach.quintile	Controle	M	257
grupo:vocab.teach.quintile	Experimental	F	368
grupo:vocab.teach.quintile	Experimental	M	345
grupo:vocab.teach.quintile	Controle		149	Not declared
grupo:vocab.teach.quintile	Controle		252	Rural
grupo:vocab.teach.quintile	Controle		114	Urbana
grupo:vocab.teach.quintile	Experimental		230	Not declared
grupo:vocab.teach.quintile	Experimental		294	Rural
grupo:vocab.teach.quintile	Experimental		189	Urbana
grupo:vocab.teach.quintile	Controle		54		Branca
grupo:vocab.teach.quintile	Controle		13		Indígena
grupo:vocab.teach.quintile	Controle		279		Not declared
grupo:vocab.teach.quintile	Controle		168		Parda
grupo:vocab.teach.quintile	Controle		1		Preta
grupo:vocab.teach.quintile	Experimental		1		Amarela
grupo:vocab.teach.quintile	Experimental		62		Branca
grupo:vocab.teach.quintile	Experimental		18		Indígena
grupo:vocab.teach.quintile	Experimental		430		Not declared
grupo:vocab.teach.quintile	Experimental		201		Parda
grupo:vocab.teach.quintile	Experimental		1		Preta
grupo:vocab.teach.quintile	Controle		146			6 ano
grupo:vocab.teach.quintile	Controle		152			7 ano
grupo:vocab.teach.quintile	Controle		100			8 ano
grupo:vocab.teach.quintile	Controle		117			9 ano
grupo:vocab.teach.quintile	Experimental		165			6 ano
grupo:vocab.teach.quintile	Experimental		196			7 ano
grupo:vocab.teach.quintile	Experimental		181			8 ano
grupo:vocab.teach.quintile	Experimental		171			9 ano
grupo:vocab.teach.quintile	Controle		88				11
grupo:vocab.teach.quintile	Controle		122				12
grupo:vocab.teach.quintile	Controle		125				13
grupo:vocab.teach.quintile	Controle		113				14
grupo:vocab.teach.quintile	Controle		43				15
grupo:vocab.teach.quintile	Controle		17				16
grupo:vocab.teach.quintile	Controle		5				17
grupo:vocab.teach.quintile	Controle		1				18
grupo:vocab.teach.quintile	Controle		1				34
grupo:vocab.teach.quintile	Experimental		119				11
grupo:vocab.teach.quintile	Experimental		155				12
grupo:vocab.teach.quintile	Experimental		159				13
grupo:vocab.teach.quintile	Experimental		155				14
grupo:vocab.teach.quintile	Experimental		80				15
grupo:vocab.teach.quintile	Experimental		27				16
grupo:vocab.teach.quintile	Experimental		9				17
grupo:vocab.teach.quintile	Experimental		3				18
grupo:vocab.teach.quintile	Experimental		1				19
grupo:vocab.teach.quintile	Experimental		1				20
grupo:vocab.teach.quintile	Experimental		1				21
grupo:vocab.teach.quintile	Experimental		1				23
grupo:vocab.teach.quintile	Experimental		1				4
grupo:vocab.teach.quintile	Experimental		1				9